My research interests broadly span the intersection of Algorithms, Machine Learning and Operations Research. I am interested
in both theory and applications of subsets of these areas.
On the theory side, I'm particularly inclined towards problems with a flavor of randomized algorithms, probability, mathematical programming, combinatorial optimization and online learning.
On the application side, I have worked on problems inspired from economics, data-mining. I have also worked on projects that "apply" some of the more theoretical results to real-world settings.
I'm always open to working on projects in other application areas that strongly draws ideas from the theory of these fields.

Previously, I obtained a B.Tech (Hons), with a major in Computer Science and a minor in Operations Research, from Indian Institute of Technology, Madras. My brother and I spent our childhood days in the beautiful city of Bangalore, India.

Abstract:
Column-sparse packing problems arise in several contexts in both deterministic and stochastic discrete optimization.
We present two unifying ideas, (non-uniform) attenuation and multiple-chance algorithms, to obtain improved approximation algorithms
for some well-known families of such problems. As three main examples, we attain the integrality gap, up to lower-order terms,
for known LP relaxations for k-column sparse packing integer programs (Bansal et al., Theory of Computing, 2012)
and stochastic k-set packing (Bansal et al., Algorithmica, 2012), and go “half the remaining distance” to optimal for a major
integrality-gap conjecture of Furedi, Kahn and Seymour on hypergraph matching (Combinatorica, 1993).

Abstract: The Online Stochastic Matching with Timeouts problem introduced by Bansal, Gupta, Li, Mestre, Nagarajan, and Rudra (Algorithmica, 2012) models
matching markets (e.g. E-Bay, Amazon). Buyers arrive from an independent and identically distributed (i.i.d.) known distribution
on buyer profiles and can be shown a list of items one at a time. Each buyer has some probability of purchasing each item and a limit
(timeout) on the number of items they can be shown. Bansal et al. (Algorithmica, 2012) gave a 0.12-competitive algorithm
which was improved by Adamczyk, Grandoni, and Mukherjee (ESA, 2015) to 0.24. We present an online attenuation framework
that uses an algorithm for offline stochastic matching as a black box. Our main contributions are as follows. On the upper bound side, we
show that this framework combined with a black box adapted from Bansal et al. (Algorithmica, 2012) yields an online algorithm which
nearly doubles the ratio to 0.46. On the lower bound side, we show that no algorithm can achieve a ratio better than 0.632 using the
common LP for this problem. We then introduce a natural generalization: Online Stochastic Matching with Two-sided Timeouts in which both
online and offline vertices have timeouts. Our framework provides the first algorithm for this problem achieving a ratio of 0.31. We
accomplish this by proposing a new black box algorithm for offline stochastic matching on star graphs, which may be of independent
interest. This new black box improves the approximation ratio for the offline stochastic matching problem on star graphs from 0.5 by
Adamczyk et al. (ESA 2015) to 0.56.

Abstract:
We develop algorithms with improved competitive ratios for some basic variants of the known i.i.d. arrival model with integral arrival rates,
including (a) the case of general weighted edges, where we improve the best-known ratio of 0.667 due to Haeupler, Mirrokni and Zadimoghaddam to 0.705 and
(b) the vertex-weighted case, where we improve the 0.7250 ratio of Jaillet and Lu to 0.7299.
We also consider two extensions, one is known I.I.D. with non-integral arrival rate and stochastic rewards.
The other is known I.I.D. b-matching with non-integral arrival rate and stochastic rewards.
We present a simple non-adaptive algorithm which works well simultaneously on the two extensions.

"Ensuring Privacy in Location Based Services: An Approach Based on Opacity Enforcement"
Joint work with Yi-Chin Wu, St`ephane Lafortune. Proceedings of the 14th International Workshop
of Discrete Event Systems (WODES), 2014

Abstract:
With the proliferation of mobile devices, Location-Based Services (LBS) that provide networked services based on users’ locations have become increasingly popular.
Such services, providing personalized and timely information, have raised privacy concerns such as unwanted revelation of users’ current locations to potential stalkers.
In this work, we show that this problem can be formally (for first time) addressed using the notion of opacity in discrete event systems. We use non-deterministic
finite-state automata to capture the mobility patterns of users and label the transitions by the location information in the queries. Using opacity verification techniques,
we show that the technique of sending cloaking queries to the server can still reveal the exact location of the user.
To enforce location privacy, we apply the opacity enforcement technique by event insertion. Specifically, we synthesize suitable insertion functions
that insert fake queries into the cloaking query sequences. The generated fake queries are always consistent with the mobility model of the user and
provably ensure privacy of the user’s current location. Finally, to minimize the overhead from fake queries, we design an optimal insertion
function that introduces minimum average number of fake queries.

Keywords: Discrete Event Systems; Formal Verification; Applications

Manuscripts

"Combinatorial Semi-Bandits with Knapsacks"
Joint Work with Alex Slivkins

Abstract:
This paper unifies two lines of work on multi-armed bandits, Bandits with Knapsacks (BwK) and semi-bandits.
The former concerns scenarios with limited "resources" consumed by the algorithm, e.g., limited inventory in a dynamic pricing problem.
The latter has a huge number of actions, but there is combinatorial structure and additional feedback which makes the problem tractable.
Both lines of work has received considerable recent attention, and are supported by numerous application examples.
We define a common generalization, and design a general algorithm for this model. Our regret rates are comparable with those for BwK and semi-bandits
in general, and essentially optimal for important special cases.

Abstract:
Bipartite matching markets pair agents on one side of a market with agents, items, or contracts on the opposing side. Prior work addresses online bipartite matching
markets, where agents arrive over time and are dynamically matched to a known set of disposable resources. In this paper, we propose a new model, Online
Matching with (offline) Reusable Resources under Known Adversarial Distributions (OM-RR-KAD), in which resources on the offline side are reusable instead of
disposable; that is, once matched, resources become available again at some point in the future. We show that our model is tractable by presenting an LP-based
adaptive algorithm that achieves an online competitive ratio of 1/2 − ε for any given ε > 0. We also show that no non-adaptive algorithm can achieve a ratio of
1/2 + o(1) based on the same benchmark LP. Through a data-driven analysis on a massive openly-available dataset, we show our model is robust enough to capture
the application of taxi dispatching service. We also present heuristics that perform well in practice.

"Assigning Tasks to Workers based on Historical Data: Online Matching with Two-sided Arrivals"
Joint Work with John Dickerson, Aravind Srinivasan, Pan Xu

Abstract:
Efficient allocation of tasks to workers is a central problem in crowdsourcing. In a typical setting, different workers arrive at different times.
Similarly, different tasks arrive at different times. The goal is to assign the available workers to tasks, such that we are able to complete as many
tasks as possible. Usually, the tasks are heterogeneous and have different rewards. Hence, we would like to maximize the total reward (as opposed to just
the number of completed tasks). Often, the arrival times of worker "types" and task "types" are not erratic and can be predicted from historical data.
In this paper, we model the problem to account for this history and propose a new mathematical model, called Online Matching with Two-Sided Arrival (OM-TSA).
We have a known bipartite graph G = (U,V,E) where U is the set of worker types and V is the set of task types. An edge e = (u, v) exists iff worker u is compatible
with task v. The setting proceeds in rounds for T time-steps. At each time-step, a worker u from U and a task v from V are sampled independently from two
respective (known) distributions. Further, we assume the sampling processes are independent and identical across the T rounds. We assume that
(1) when a worker u arrives, they stay in the sys- tem until matched and (2) when a task v arrives, it needs to be assigned to an available worker u
immediately (and obtaining a reward w(u, v)); if not, we do not get any reward for this task (and the action is irrevocable). Our goal is to maximize the
expected reward. Our model is a significant generalization of the classical online matching model, where one of the sets U and V , say U, is assumed to be
available offline. We relax this assump- tion to capture the transient nature of worker arrivals. For the general version of OM-TSA, we present an optimal
non-adaptive algorithm which achieves an online competitive ratio of 0.295. For the special case of OM-TSA where the reward is a function of just the worker type,
we present an improved algorithm (which is adaptive) and achieves a competitive ratio of at least 0.345. On the hardness side, along with showing that the ratio
obtained by our non-adaptive algorithm is the best possible among all non-adaptive algorithms, we further show that no (adaptive) algorithm can achieve a ratio better
than 0.581 (unconditionally), even for the special case of OM-TSA with homogenous tasks (i.e., all rewards are same). At the heart of our analysis lies a new technical
tool (which is a refined notion of the birth-death process) called the two-stage birth-death process, which may be of independent interest. Finally, we perform
numerical experiments on both simulated as well as two real-world datasets obtained from crowdsourcing platforms to complement our theoretical results.

IBM Almaden Research Center, San Jose

I spent the Summer of 2015 at Adobe Systems Inc, at their San Jose office. I worked with the Algorithms team inside the Digital Marketing team headed by Anil Kamath.
I was involved in an applied algorithms project in databases. I was working on Online Entity Resolution problem.

Miscellanous Writings - Academic and Non-Academic

Final project for the Computational Journalism class on Bias in Google's Autocomplete Suggestion.
Along with Daniel Trielli, we studied the bias in auto-complete suggestions of various country specific
domains of Google when searched about entities of other countries.
(For example, searching about Pakistan in India-specific Google page).
You can find the code used for the experiments here.
Article summarizing the methodologies and findings

Final class project for the Convex Optimization class.
Along with Manasij Venkatesh and Aditya Acharya, we looked at sparse representations for speech
using Convex Optimization based approximations. In particular, we applied techniques from compressed
sensing literature to speech. Report, Poster,
code

Final class project for the Lower Bounds in Algorithms class.
Along with Tommy Pensyl and Bartosz Rybicki (visiting from Poland), we studied
lower bounds for fault tolerant variations of the facility location problem. Report